Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm

Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 6; s. 1123
Hlavní autoři:	Hu, Shimin, Fong, Simon, Yang, Lili, Yang, Shuang-Hua, Dey, Nilanjan, Millham, Richard C., Fiaidhi, Jinan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 16.03.2021
Témata:	Accuracy Algorithms Approximation Approximation method ASTER Classification Data analysis Data mining Data processing data stream mining Data transmission Emergency procedures Evolution Evolutionary algorithms evolutionary computing feature selection Ground stations image analysis Image classification landscapes Latency Learning algorithms Machine learning Military operations monitoring Multivariate analysis Object recognition Optimization Pattern recognition Remote sensing Satellites Subgroups Subspaces Surveillance
ISSN:	2072-4292, 2072-4292
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Abstract	Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%.
AbstractList	Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%.
Author	Fong, Simon Yang, Shuang-Hua Fiaidhi, Jinan Hu, Shimin Yang, Lili Dey, Nilanjan Millham, Richard C.
Author_xml	– sequence: 1 givenname: Shimin surname: Hu fullname: Hu, Shimin – sequence: 2 givenname: Simon surname: Fong fullname: Fong, Simon – sequence: 3 givenname: Lili surname: Yang fullname: Yang, Lili – sequence: 4 givenname: Shuang-Hua surname: Yang fullname: Yang, Shuang-Hua – sequence: 5 givenname: Nilanjan surname: Dey fullname: Dey, Nilanjan – sequence: 6 givenname: Richard C. orcidid: 0000-0002-7970-9615 surname: Millham fullname: Millham, Richard C. – sequence: 7 givenname: Jinan surname: Fiaidhi fullname: Fiaidhi, Jinan
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SubjectTerms	Accuracy Algorithms Approximation Approximation method ASTER Classification Data analysis Data mining Data processing data stream mining Data transmission Emergency procedures Evolution Evolutionary algorithms evolutionary computing feature selection Ground stations image analysis Image classification landscapes Latency Learning algorithms Machine learning Military operations monitoring Multivariate analysis Object recognition Optimization Pattern recognition Remote sensing Satellites Subgroups Subspaces Surveillance
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Title	Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm
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